AssociationRule
We consider association rules of the form "
- Value parameters:
- affinity
Also known as Jaccard Similarity, affinity is a measure of the transactions that contain both the antecedent and consequent (intersect) compared to those that contain the antecedent or the consequent (union): affinity(A->C) = support(A+C) / [ support(A) + support(C) - support(A+C)]
- antecedent
The id value of the itemset which is the antecedent of the rule. We represent the itemset by the letter A.
- confidence
The confidence of the rule: confidence(A->C) = support(A+C) / support(A)
- consequent
The id value of the itemset which is the consequent of the rule. We represent the itemset by the letter C.
- id
An identification to uniquely identify an association rule.
- leverage
Another measure of interestingness is leverage. An association with higher frequency and lower lift may be more interesting than an alternative rule with lower frequency and higher lift. The former can be more important in practice because it applies to more cases. The value is the difference between the observed frequency of A+C and the frequency that would be expected if A and C were independent: leverage(A->C) = support(A->C) - support(A)*support(C)
- lift
A very popular measure of interestingness of a rule is lift. Lift values greater than 1.0 indicate that transactions containing A tend to contain C more often than transactions that do not contain A: lift(A->C) = confidence(A->C) / support(C)
- support
The support of the rule, that is, the relative frequency of transactions that contain A and C: support(A->C) = support(A+C)